Articles

Long-form thinking on AI-native operating models, strategy, and transformation, by Dan Walter.

• Token efficiency in Claude Code: why the week runs out early, and why the fix is mostly free — A Claude Code subscription that empties before the week is out is rarely a wrong setting. The limit hands out an opaque rate against two clocks and one shared pool; what drains it is structural — every turn resends the whole conversation, so a task's cost grows with about the square of its turn count, and prompt caching quietly saves more than any dial the user touches. This report ranks the real cost drivers, explains the turns-squared mechanism, gives the honest verdict on whether a heavy CLAUDE.md is the culprit, and orders the levers cheapest-first — the free ones at the top.
• Choosing a Claude model in Claude Code: why the cheaper one is usually the expensive one — Picking a model in Claude Code looks like a price decision and isn't. It is two independent dials — which model, and how hard it thinks — and the only number that should move either is the cost of finishing the whole task, not the price on the token. This report explains why a cheaper model is so often the expensive one (the turn-count mechanism that makes weak models superlinearly costly on hard work), how to set both dials from four properties of the task, why the effort dial pays off on different curves for different work, and why a benchmark score is a weak guide when real performance is model × harness × product.
• The long window — An AI-native operating model — a person directing a fleet of agents that design, ship and run the system — is a once-in-a-lifetime, compounding advantage, and adoption lags what the technology deserves, which is exactly why the early-mover window is real. The instinct that stops legacy organisations from taking it is ambiguity aversion: under a short knowability horizon we draw inward and avoid rather than evaluate. This memo reframes the migration as a tractable risk-management problem — identify the events, rate the inherent risk, install the control, read the residual — and is surgical about scope: only four risks actually change when agents do the work. The operating model is guardrails, not gates, applied at four chokepoints; and a closing chapter draws the clean line for regulated industries, where the rule is that regulation attaches to a data type and an activity, not to a brand.
• Building an AI-native company — Just as digital natives disrupted bricks-and-mortar, AI-native companies will now disrupt digital natives and incumbents alike. This is the operating blueprint: the spectrum of postures, the four-layer pyramid, protocols as the unit of leverage — with case studies of a law firm, a food company, and a vaccine maker woven through the argument.
• The great compounding — Three months after ChatGPT launched, I watched a room full of Google executives quietly reorganise their lives around it. That, not any demo, is what convinced me. Two and a half years later I'm all-in — building AI-native systems, and thinking hard about what compounding intelligence means for science, for society, and for business. This is where I've landed, and why everything else I'm publishing starts here.